System and method for analyzing analyst recommendations on a single stock basis

ABSTRACT

A system and method for measuring and creating a score for the performance of one or more contributor recommendations on a single stock. According to one embodiment, the score may be derived via a payoff function that depends on a variety of factors. For example, the factors may include one or more of: i) the performance of the stock; ii) the performance of a selected benchmark; iii) the recommendation of the contributor for the stock; and/or iv) other factors. According to one embodiment the payoff function may be designed such that certain desired characteristics are satisfied.

FIELD OF THE INVENTION

The invention relates to a system and method for measuring and creatinga score for the performance of securities analysts' recommendations on asingle stock basis.

BACKGROUND OF THE INVENTION

Contributors make recommendations on stocks taking into account variousfactors including predicted price appreciation. A contributor mayinclude an analyst, a group of analysts, a brokerage firm, or othercontributors. Contributors may convey their recommendations via arecommendation scheme. A recommendation scheme generally will includevarious-numbers of recommendation levels and a label for each level. Forexample, a simple three level recommendation scheme may include apositive, negative or neutral. A five level recommendation scheme mayinclude a buy, a strong buy, a sell, a strong sell or a hold. Moregenerally, a recommendation scheme may include one or morerecommendations for predicted positive returns, a recommendation forreturns that are predicted to be neutral, and one or morerecommendations for predicted negative returns. A recommendation schememay include recommendations relative to a benchmark such as anoverweight recommendation for predicted positive returns, an inlinerecommendation for predicted neutral returns, and an underweightrecommendation for predicted negative returns. Other recommendationschemes and labels are known.

For simplicity, as used herein three level recommendation schemes may begenerically represented as including a positive recommendation, aneutral recommendation, and a negative recommendation, and five levelrecommendation schemes may be generically represented as including amore positive recommendation, a positive recommendation, a neutralrecommendation, a negative recommendation, and a more negativerecommendation. Except as specifically indicated otherwise, this is notintended to exclude other labels.

Different contributors may use recommendation schemes that seem similarbased on labels, but may actually correspond to differentbenchmark-relative returns. For example, a first contributor may use afive level recommendation scheme. A second contributor may use a threelevel recommendation scheme wherein a positive recommendationcorresponds to a predicted benchmark-relative return similar to that ofa positive recommendation in the five level recommendation scheme of thefirst recommendation, and a negative recommendation corresponds to abenchmark-relative return similar to that of a negative recommendationin the five level recommendation scheme of the first contributor. Athird contributor may use a three level recommendation scheme wherein apositive recommendation corresponds to a benchmark-relative returnsimilar to that of a more positive recommendation in the five levelrecommendation scheme of the first contributor, and a negativerecommendation corresponds to a benchmark-relative return similar tothat of a more negative recommendation in the five level recommendationscheme of the first contributor. In other words, if a five-level schemehas ratings 1-5 (from most positive to most negative) a three levelscheme may correspond to levels 1, 3, 5 or it may correspond to 2, 3, 4.

Different contributors may make recommendations based on predictions ofbenchmark-relative returns that are relative to different benchmarks.Benchmarks may be fixed benchmarks or variable benchmarks. For example,a fixed benchmark may be 5% for a positive and 15% for a more positiveat one contributor and other percentages for another contributor.Variable benchmarks may be a market index benchmark related to stockvaluations of stocks associated with a market index, an industrybenchmark related to stock valuations of stocks within a given industry,or other variable benchmarks.

Various methods exist for measuring the performance of a contributor onall or a large number of stocks via a portfolio calculation approach.These systems typically create a simulated portfolio for a set (e.g.,those in an industry classification) of each contributors stocks wherethe portfolio mimics owning “positive-rated” stocks and shortingnegative-rated stocks. These systems may not answer the question ofwhich contributor is best at rating a single stock. Further, thesesystems may merely yield a possible percent return, and not a scorerepresentative of an accuracy of the analyst in predicting future stockvaluation. Moreover, a percent return alone can be misleading, dependingon the performance of other stocks or benchmarks. These systems usuallyrequire a minimum number of stocks followed in the set (e.g.,semiconductor industry) to be scored and ranked on that set. In knownsystems, stocks that have little variation in value may be difficult tocompare with other stocks, or they may not take into account separatebenchmarks for separate stocks.

Another drawback of existing systems is how to evaluate a neutralrecommendation. In a percent return analysis a neutral rating may yieldlittle “return” but may be a very good prediction. Other problems anddrawbacks also exist.

Therefore, there exists a need for scoring a contributor for theperformance of recommendations by the analyst or analysts with respectto a single stock.

SUMMARY

One aspect of the invention relates to a system and method for measuringand creating a score for the performance of one or more contributorrecommendations on a single stock. According to one embodiment, thescore may be derived via a payoff function that depends on a variety offactors. For example, the factors may include one or more of: i) theperformance of the stock; ii) the performance of a selected benchmark;iii) the recommendation of the contributor for the stock; and/or iv)other factors.

Different types of recommendations may have different payoff functions.According to one embodiment the payoff functions may be designed suchthat certain desired characteristics are satisfied. For example,according to one approach, and where the contributor selects therecommendation from a set of five recommendation types, the desiredcharacteristics may include one or more of ensuring that: i) a positiverecommendation can score higher than a more positive recommendation fora “modest positive” stock return; ii) a neutral recommendation can scorehigher than a positive recommendation and a negative recommendation(i.e. a neutral can be the single best call on a stock) for certainstock return scenarios; iii) a negative recommendation can score higherthan a more negative recommendation for a “modest negative” stockreturn; iv) a more positive recommendation can score higher than apositive recommendation for a “significantly positive” stock return; andv) a more negative recommendation can score higher than a negativerecommendation for a “significantly negative” stock return.

It will be appreciated that while the embodiment of the inventiondescribed above may apply where contributors select from a set ofrecommendations that includes five distinct types of recommendations, analternative number of types of recommendations may be accounted for. Forexample, in some instances, an contributor may select from a set ofthree recommendations, such as a negative recommendation, a neutralrecommendation, and a positive recommendation. In this case, the payofffunction may be designed such that certain desired characteristics aresatisfied. For example, that a neutral recommendation can score higherthan a positive recommendation or a negative recommendation (i.e. aneutral can be the single best call on a stock) for certain stock returnscenarios. Various aspects of the invention can be used with otherrecommendation schemes that have other numbers of types ofrecommendations. Other labels can also be used for the recommendations.

According to various embodiments, a score associated with arecommendation with respect to a stock may be determined as a functionof a benchmark-relative return. The benchmark-relative return mayinclude the return of the stock relative to a benchmark return. Thereturn of the stock may include a price change of the stock, a dividendassociated with the stock, other distributions associated with thestock, or other returns associated with the stock. In some instances,the benchmark return may include a variable benchmark return. Thevariable benchmark return may be a market index benchmark return thatcorresponds to the return of stocks associated with a market index. Or,the benchmark return may be related to a sector or industry benchmarkthat may correspond to a return of the stocks within a sector orindustry group. The benchmark return may be related to a geographicalbenchmark that may correspond to a return of the stocks within ageographic region associated with the stock. In other instances, thebenchmark return may include a fixed benchmark return, such as a fixedpercent increase per unit of time, or other fixed benchmark return. Forexample, the benchmark return for a positive may be set equal to areturn of 5% annually. The benchmark return may be set automatically, ormay be selected by a user. Other fixed or variable benchmark returns maybe used.

More specifically, according to one embodiment of the invention, apayoff function may describe a score for a recommendation as a functionof a benchmark-relative return on a stock. For example, a payofffunction for a positive recommendation may have a zero value at or neara zero benchmark-relative return and may slope in a positive directionwith respect to a benchmark-relative return axis; a payoff function fora more positive recommendation on a stock may have a zero value at amore positive benchmark-relative return, located away from the zerobenchmark-relative return in a positive direction, and may be moresteeply sloped in a positive direction than the payoff function for thepositive recommendation. This implies that the payoff functions forpositive recommendations and more positive recommendations may cross andthat for recommendations on stocks with small positivebenchmark-relative returns, a positive recommendation may be associatedwith a higher score than a more positive recommendation would. Amirror-image situation may exist for a negative recommendation and amore negative recommendation.

According to one aspect of the invention, the payoff function for aneutral recommendation on the stock may yield a maximum score for theneutral recommendation at or near the zero benchmark-relative return,and lower scores as the benchmark-relative return increases or decreasesaway from the zero benchmark-relative return axis. This implies that thescore for a neutral recommendation may be higher than the score thatwould be received for a positive recommendation, a more positiverecommendation, a negative recommendation, or a more negativerecommendation on the same stock, where the stock provides a modestbenchmark-relative return. The criteria for defining a modest return canbe specified or user selected. Generally it may be desirable for this toinclude a zero benchmark-relative return plus or minus a range where aneutral recommendation will yield the highest score. Various otherparameters of one or more of the payoff functions may selected by auser, or may be determined automatically.

In some embodiments, one or more of the parameters of one or more of thepayoff functions may be determined based on one or more aspects of arecommendation scheme associated with a recommendation. For example, fordifferent contributors, a positive recommendation may imply an expected5% return, while for others it may imply a 1% return. Or, differentcontributors may determine benchmark-relative return in relationship toseparate benchmarks. Other differences may apply. The differences may betaken into account in establishing one or more pay off functions foreach contributor-specific recommendation scheme. According to apreferred embodiment, a contributor-specific set of parameters may beused to determined the payoff functions of recommendations used by thecontributor.

In another embodiment, recommendations made by different contributorsbased on different recommendation schemes may be standardized.Standardizing recommendations may include mapping the recommendationsinto a reference recommendation scheme to facilitate comparisons and forother purposes. The recommendations mapped into the referencerecommendation scheme may then be scored according to one or more payoutfunctions with a set of parameters that correspond to the referencerecommendation scheme.

In some embodiments of the invention, scores associated with a pluralityof individual recommendations may be aggregated into a single score.Aggregating the plurality of scores may provide a collective indicatorof the performance of the recommendations. For example, a plurality ofscores associated with recommendations made by a contributor withrespect to a stock over a selected time period may provide a morecomplete indicator of a performance of the contributor in predictingfuture valuation of the stock. In other embodiments, a plurality ofrecommendations made by a set of contributors with respect to a singlestock may be aggregated. Other aggregations may be made.

According to another aspect of the invention, the system and method mayalso enable the score for contributor recommendations on a single stockto be normalized. For example, the normalization may be based on the“opportunity” to earn points or other factors. According to oneembodiment, the normalization may be based on one or morecharacteristics of the stock, or other factors. For example, thenormalization may be based on a volatility of a stock. This may allowfor a more meaningful relative comparison of performance.

Another aspect of the invention relates to a roll-up procedure. Theroll-up procedure may enable a combined score related to a set of stocksto be created from a set of scores measured and created at the singlestock level. According to one embodiment, each single stock level scoremay be normalized and then used in the combined score. The combinedscore may be used to score all or some of a set of contributors, suchas, a set of analysts associated with a single broker a set of brokers,or other sets of contributors.

Another aspect of the invention relates to a system for implementingvarious aspects of the invention. The system may include one or moreprocessing sections, one or more data sources, one or more storagesections, and one or more remote terminals.

The processing section may include a benchmark-relative return module, apayoff function module, a score determination module, and a userinterface module. The benchmark relative-return module may determine abenchmark-relative return of a stock associated with a recommendationbeing scored. The payoff function module may, store, configure, manage,or provide other functionality with respect to one or more payofffunctions that may be used to create scores for recommendations. Thescore determination module may determine a score with respect to asingle recommendation according to a payoff function provided by thepayoff function module. The score determination module may determine anaggregate score based on an aggregation of scores, may normalize one ormore scores, may roll-up one or more scores, or may perform otherfunctions. The user interface module may enable a user to create, edit,and/or review one or more payoff functions. The user interface modulemay enable the user to request calculations of one or more scoresincluding an individual score, an aggregated score, a rolled-up score,or other scores. The user interface module may enable the user to viewresults generated by the system.

The data sources may provide one or more types of information to thesystem. The data sources may include a market information section, and arecommendation information section. The market information section mayprovide stock valuation information to the system. The marketinformation section may provide stock valuation information in responseto an information query, or may provide information in a streamedmanner. The recommendation information section may providerecommendation information to the system. Recommendation information mayinclude a recommendation source, a stock name associated with arecommendation, a recommendation type, a date a recommendation was made,a time period associated with a recommendation, or other recommendationinformation. Or, the data sources may provide other information to theprocessing section.

According to one embodiment, the system may include a storage section.The storage section may include one or more storage components (e.g.databases). The storage section may receive and store various kinds ofinformation. For example, the storage section may store informationreceived from the various data sources including market information,recommendation information, previously generated scores, or otherinformation. The storage section may also store other information, suchas previously generated scores and other information.

Another aspect of the invention may relate to a method of creating ascore for a single recommendation. The method may include arecommendation information operation, a stock return determinationoperation, a benchmark return determination operation, abenchmark-relative return determination operation, and a scoredetermination operation.

At the recommendation information operation, recommendation informationmay be acquired. Recommendation information may include a recommendation(e.g. positive, negative, neutral, etc.), a stock associated with therecommendation, a time period associated with the recommendation, orother recommendation information.

At the stock return determination operation, a return of the stockassociated with the recommendation may be determined. The return of thestock may be determined for the time period associated with therecommendation.

A benchmark return may be determined at the benchmark returndetermination operation. The benchmark return may be determined for thetime period associated with the recommendation.

At the benchmark-relative return operation the benchmark-relative returnmay be determined. The benchmark-relative return may be determined bysubtracting the return of the benchmark from the return of the stock.

A score for the recommendation may be determined at the scoredetermination operation. The score may be determined by applying apayoff function that corresponds with the recommendation to thebenchmark-relative return.

Another aspect of the invention may relate to a method of determining anaggregated score associated with a plurality of recommendations on asingle stock. The method may include an individual score determinationoperation, an individual score summing operation, an individual scorecompletion operation, an individual score averaging operation, and anormalizing operation.

At the individual score determination operation, a score for anindividual recommendation from the plurality of recommendations may bedetermined. The score for the individual recommendation may bedetermined according to the method of creating a score for a singlerecommendation.

The score for the individual recommendation may be combined withpreviously determined scores at the individual score summing operation.The individual recommendations may be combined by addition.

At the individual score completion determination operation adetermination may be made as to whether all of the recommendation in theplurality of recommendations have been scored and summed. If all of therecommendations have not been scored and summed, the method may returnto the individual score determination operation to determine a score foranother individual recommendation. If all of the recommendations havebeen scored and summed, the method may proceed to the individual scoreaveraging operation.

The sum of the scores of the individual recommendations may be averagedat the individual score averaging operation. The scores may be averagedby dividing the sum of the scores by the number of recommendations inthe plurality of recommendations.

At the normalizing operation, the averaged score may be normalized. Theaveraged score may be normalized by the opportunity associated with thestock for the time periods associated with the recommendations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary embodiment of a plurality of payofffunctions.

FIG. 2 illustrates an exemplary embodiment of a system for creating ascore for a contributor for the performance of the contributor'srecommendation on a single stock.

FIG. 3 illustrates an exemplary embodiment of an interface.

FIG. 4 illustrates an operation according to various embodiments of theinvention.

FIG. 5 illustrates an operation according to various embodiments of theinvention.

FIG. 6 illustrates an operation according to various embodiments of theinvention.

DETAILED DESCRIPTION OF THE DRAWINGS

One aspect of the invention relates to a system and method for measuringand creating a score for the performance of one or more of acontributor's recommendations on a single stock. According to oneembodiment, the score may be derived via a payoff function that dependson a variety of factors. For example, the factors may include one ormore of: i) the performance of the stock; ii) the performance of aselected benchmark; iii) the recommendation of the contributor for thestock; and/or iv) other factors.

Different types of recommendations may have different payoff functions.According to one embodiment the payoff functions may be designed suchthat certain desired characteristics are satisfied. For example,according to one approach, and where the contributor selects therecommendation from a set of five recommendation types, the desiredcharacteristics may include one or more of ensuring that: i) a positiverecommendation can score higher than a more positive recommendation fora “modest positive” stock return; ii) a neutral recommendation can scorehigher than a positive recommendation and a negative recommendation(i.e. a neutral can be the single best call on a stock) for certainstock return scenarios; iii) a negative recommendation can score higherthan a more negative recommendation for a “modest negative” stockreturn; iv) a more positive recommendation can score higher than apositive recommendation for a “significantly positive” stock return; andv) a more negative recommendation can score higher than a negativerecommendation for a “significantly negative” stock return.

FIG. 1 illustrates an exemplary embodiment of a plurality of payofffunctions 110. Payoff functions 110 may describe a score for arecommendation as a function of a benchmark-relative return on a stock.For example, a positive payoff function 112 may describe a score for apositive recommendation. Positive payoff function 112 may have apositive zero value 118 at or near a zero benchmark-relative return axis116, and may slope in a positive direction with respect to abenchmark-relative return axis at a positive slope. Positive zero value118 may represent a benchmark-relative return for which positive payofffunction 112 may yield a score of zero.

In some embodiments of the invention, payoff functions 110 may include amore positive payoff function 120. More positive payoff function 120 maycorrespond to a more positive recommendation on a stock. More positivepayoff function 120 may have a more positive zero value 122 at apositive benchmark-relative return, and may slope in a positivedirection with respect to benchmark-relative return axis 114 at a morepositive slope. More positive zero value 122 may represent a higherbenchmark-relative return than positive zero value 118. The morepositive slope may be steeper than the positive slope.

As illustrated in FIG. 1, according to one embodiment of the invention,positive payoff function 112 and more positive payoff function 120 maycross at a more positive/positive intersection 124. Thus, for modestpositive benchmark-relative stock returns, such as stockbenchmark-relative returns that correspond to a more negativebenchmark-relative return than the more positive/positive intersection124, a positive recommendation may be associated with a higher scorethan a more positive recommendation would.

Various parameters of positive payoff function 112 and/or more positivepayoff function 120 may be determined as default settings. Or, one ormore of the parameters may be configured by a user. The parameters mayinclude positive zero value 118, more positive zero value 122, thepositive slope, the more positive slope, more positive/positiveintersection 124, or other parameters.

In a preferable embodiment, a mirror-image situation of negative payofffunction 112 and more negative payoff function 120 may exist for anegative payoff function 126 and a more negative payoff function 128.More specifically, negative payoff function 126 for describing a scoreof a negative recommendation may have a negative zero value 130 at ornear the zero benchmark-relative return, and may slope in a negativedirection with respect to benchmark-relative return axis 114 at anegative slope.

According to various embodiments of the invention, payoff functions 110may include a more negative payoff function 128. More negative payofffunction 128 may correspond to a more negative recommendation on astock. More negative payoff function 128 may have a more negative zerovalue at a negative benchmark-relative return, and may slope in anegative direction with respect to benchmark-relative return axis 114 ata more negative slope. The more negative slope may be steeper than thenegative slope. More negative zero value 132 may correspond to a lowerbenchmark-relative return than negative zero value 130.

As illustrated in FIG. 1, according to one embodiment of the invention,negative payoff function 126 and more negative payoff function 128 maycross at a more negative/negative intersection 134. Thus, for modestnegative benchmark-relative returns, such as stock benchmark-relativereturns greater than more negative/negative intersection 134, a negativerecommendation may be associated with a higher score than a morenegative recommendation would.

Various parameters of negative payoff function 126 and/or more negativepayoff function 128 may determined as default settings. Or, one or moreof the parameters may be configured by a user. The parameters mayinclude negative zero value 130, more negative zero value 132, thenegative slope, the more negative slope, more negative/negativeintersection 134, or other parameters.

Additionally, payoff functions 110 may include a neutral payoff function136 for a neutral recommendation on a stock. Neutral payoff function 136may be configured such that, where a stock provides a modestbenchmark-relative return, a score for a neutral recommendation may behigher than a score for a positive recommendation, a more positiverecommendation, a negative recommendation, or a more negativerecommendation on the same stock. Neutral payoff function 136 mayintersect with positive payoff function 112 at a positive hurdle point142. For stock returns more negative than that of positive hurdle point142, a neutral recommendation may yield a higher score than a positiverecommendation. neutral payoff function 136 may intersect with morepositive payoff function 120 at a more positive hurdle point 144. Forstock returns more negative than that of more positive hurdle point 144,a neutral recommendation may yield a higher score than a more positiverecommendation. Similarly, neutral payoff function 136 may intersectwith negative payoff function 126 at a negative hurdle point 146. Forstock returns more positive than that of negative hurdle point 146, aneutral recommendation may yield a higher score than a negativerecommendation. Neutral payoff function 136 may intersect with morenegative payoff function 128 at a more negative hurdle point 148. Forstock returns more positive than that of more negative hurdle point 148,a neutral recommendation may yield a higher score than a more negativerecommendation.

In preferred embodiments of the invention, neutral payoff function 136may yield a maximum score for a neutral recommendation at a maximumpoint 138. Maximum point 138 may occur at or near zero%benchmark-relative-return axis 116. Neutral payoff function 136 maytaper off and yield lower scores as the benchmark-relative return of thestock increases or decreases. Neutral payoff function 136 may taper offto a minimum value 140. Tapering off to minimum value 140 may includeexponentially decaying to minimum value 140. It will be appreciated thatalthough Neutral payoff function 136 is illustrated as a curve, otherconfigurations may be employed. For example, neutral payoff function 136may be configured to resemble a triangle function that comes to a peakat maximum point 138. Other configurations exist.

Various parameters of neutral payoff function 136 may determined asdefault settings. Or, one or more of the parameters may be configured bya user. The parameters may include a score of maximum point 138, abenchmark-relative return at which maximum point 138 occurs, minimumvalue 140, a shape of neutral payoff function 136, positive hurdle point142, more positive hurdle point 144, negative hurdle point 146, morenegative hurdle point 148, or other parameters.

An exemplary embodiment of a positive payoff function may be describedas:

P _(Bst)=max(MIN_(B), (R _(st) −Z _(B))*S _(B)),   (1)

where MIN_(B) may represent the minimum payout for a positiverecommendation; R_(st) may represent a benchmark-relative return of thestock for a time period related to the recommendation; Z_(B) mayrepresent a zero cross for the positive recommendation; and S_(B) mayrepresent the slope for the positive recommendation. The zero cross fora positive recommendation may be equal determined according to thefollowing equation:

$\begin{matrix}{{Z_{B} = {{\hat{Z}}_{B}\sqrt{\frac{T_{t}}{T_{year}}}}},} & (1)\end{matrix}$

where {circumflex over (Z)}_(B) may represent a configurable positivezero cross for a positive recommendation that may be associated with atime period of the benchmark, which may be selectable by the user; T_(t)may represent a number of weekdays associated with the positiverecommendation; and T_(year) may represent the number of weekdaysassociated with the benchmark.

An exemplary embodiment of a more positive payout function may bedescribed as:

P _(SBst)=max(MIN_(SB), (R _(st) −Z _(SB))*S _(SB)),   (1)

where MIN_(SB) may represent a minimum score for a more positiverecommendation; R_(st) may represent a benchmark-relative return of thestock for a time period related to the recommendation; Z_(SB) mayrepresent a zero cross for the more positive recommendation; and S_(SB)may represent a more positive slope. The zero cross for the morepositive recommendation may be determined in a similar manner to thezero cross for the positive recommendation illustrated above.

An exemplary embodiment of a negative payoff function may be describedas:

P _(S st)=max(MIN_(S), (R _(st) −Z _(S))*S _(S)),   (1)

where MIN_(S′) may represent a minimum score for a negativerecommendation; R_(st) may represent a benchmark-relative return of thestock for a time period related to the negative recommendation; Z_(S)may represent a zero cross for the negative recommendation; and S_(S)may represent a negative slope.

An exemplary embodiment of a more negative payout function may bedescribed as:

P _(SS st)=max(MIN_(SS), (R _(st) −Z _(SS))*S _(SS)),   (1)

where MIN_(SS) may represent a minimum score for a more negativerecommendation; R_(st) may represent a benchmark-relative return of thestock for a time period related to the more negative recommendation;Z_(SS) may represent a zero cross for the more negative recommendation;S_(SS) and may represent a more negative slope.

It will be appreciated that while the embodiment of the inventiondescribed above may apply where contributors select from a set ofrecommendations that includes five distinct types of recommendations, analternative number of types of recommendations may be accounted for. Forexample, in some instances, a contributor may select from a set ofrecommendations that includes a negative recommendation, a neutralrecommendation, and a positive recommendation. Or, a contributor mayselect from a set of recommendations that includes a more negativerecommendation, a negative recommendation, a weak negativerecommendation, a neutral recommendation, a weak positiverecommendation, a positive recommendation, and a more positiverecommendation. Other sets of recommendations exist.

In some embodiments, one or more of the parameters of one or more of thepayoff functions may be determined based on one or more aspects of arecommendation scheme associated with a recommendation. For example, anaspect of a recommendation scheme may include a benchmark-relativereturn associated with a more positive recommendation within therecommendation scheme, a benchmark-relative return associated with apositive recommendation within the recommendation scheme, abenchmark-relative return associated with a negative recommendationwithin the recommendation scheme, a benchmark-relative return associatedwith a more negative within the recommendation scheme, a benchmark usedto determine a benchmark-relative return within the recommendationscheme, or other aspects of the recommendation scheme. According to apreferred embodiment, a contributor specific set of parameters may bedetermined according to a recommendation scheme used by a contributor.

In another embodiment, recommendations made within a plurality ofrecommendation schemes including different aspects may be standardized.Standardizing a recommendation may include mapping the recommendationinto a reference recommendation scheme by mapping the aspects of therecommendation scheme in which the recommendation was made into theaspects of the reference recommendation scheme. The recommendationsmapped into the reference recommendation scheme may then be scoredaccording to one or more payout functions with a set of parameters thatcorrespond to the reference recommendation scheme.

FIG. 2 illustrates an exemplary embodiment of a system 210 forimplementing various aspects of the invention. System 210 may include aprocessing section 212. Processing section 212 may process informationto create one or more scores for contributor recommendations. Processingsection 212 may include a benchmark-relative return module 214.Benchmark-relative return module 214 may determine a benchmark-relativereturn of a stock associated with a recommendation being scored.Processing section 212 may include a payoff function module 216. Payofffunction module 216 may store, configure, manage, or provide otherfunctionality with respect to one or more payoff functions that may beused to create scores for recommendations. Processing section 212 mayinclude a score determination module 218. Score determination module 218may determine scores for recommendations based on information receivedfrom other modules within processing section 212 and informationreceived from outside processing section 212. Score determination module218 may determine a score with respect to a single recommendation, maydetermine an aggregate score based on an aggregation of scores, maynormalize one or more scores, may roll-up one or more scores, or mayperform other functionalities. Although benchmark-relative return module214, payoff function module 216, and score determination module 218 areillustrated in FIG. 2 as being present at processing section 212, itwill be appreciated that some or all of the functionality provided bythe various modules may be located remotely with respect to processingsection 212.

In some embodiments, system 210 may include one or more data sources219. Data sources 219. Data sources 219 may be operatively linked withprocessing section 212 such that data may be provided from data sources219 to processing section 212. For example, data sources 219 may includea market information section 220, a recommendation information section222, or may provide other data to processing section 212 from otherinformation section 223.

Market information section 220 may provide stock valuation informationto processing section 210. Market information section 220 mayselectively provide stock valuation information, such as for a selectedstock or group of stocks, or market information section may provide anindiscriminate feed of stock market information, or other stockvaluation information to processing section 212.

According to one embodiment, recommendation information section 222 mayprovide information regarding one or more recommendations to processingsection 212. For example, recommendation information section 222 mayprovide recommendation information to processing section 212 asrecommendations are made, may provide recommendation corresponding topreviously made recommendations, or provide other recommendationinformation to processing section 212 so that recommendations may bescored by system 210. Recommendation information may include arecommendation source, such as a contributor, a stock name associatedwith a recommendation, a recommendation type, or other recommendationinformation.

In various embodiments of the invention, system 210 may include astorage section 224. Storage section 224 may receive and store variouskinds of information. For example, storage section 224 may store marketinformation, recommendation information, previously generated scores, orother information. Storage section 224 may include one or more storagecomponents (e.g. databases) configured to store the information. Storagesection 224 may provide previously stored information to system 210 atvarious times. For example, storage section 224 may provide previouslycreated scores to processing section 212 for aggregation by scoredetermination module 218. Or, storage section 224 may provide storedmarket information and/or recommendation information to processingsection 212. It will be appreciated that storage section 224 may beassociated with a single physical location, or storage section 224 mayinclude a plurality of storage modules located remotely from each other.For example, storage section 224 may include a score storage module, amarket information storage module, a recommendation information storagemodule, a user profile storage module for storing one or more userprofiles associated with a plurality of users, or other storage modules.

According to a preferred embodiment, system 210 may include an interfacemodule 226. User interface module 226 may enable a user to create, edit,and/or review one or more payoff functions. User interface module 226may enable the user to request calculations of one or more scoresincluding an individual score, an aggregated score, a rolled-up score,or other scores. User interface module 226 may enable the user to viewresults generated by system 210.

In some embodiments, system 210 may include one or more remote terminals228. Remote terminal 228 may enable a user to access system 210. Remoteterminal 228 may access system 210 via interface module 226. Access viainterface module 226 may be enables via a graphical user interface, orother remote interface.

In the exemplary embodiment illustrated in FIG. 2, the variouscomponents of system 210 may be operatively linked via a network. Insuch embodiments, system 210 may include one or more servers 230 thatmay include processing section 212, storage section 224, and/orinterface 226. Remote terminal 228 may include a client that may includea graphical interface that may provide a user with access to system 210.It will be appreciated that various components illustrated as beinginclude in server 230 may be relocated to run on remote terminal 228.

According to various embodiments, the benchmark-relative return, asdetermined by benchmark relative return module 214, may include thereturn of the stock relative to a benchmark return associated with thestock. In some instances, the benchmark return may be related to a fixedbenchmark hurdle, such as a fixed percent increase per unit of time, orother fixed benchmark return. In other instances, the benchmark returnmay be related to a market benchmark return. The market benchmark maycorrespond to the return of stocks associated with a market that thestock is associated with. Or, the benchmark return may be related to asector or industry benchmark that may correspond to a return of thestocks within a sector or industry group associated with the stock. Inone embodiment, the benchmark return may be related to a geographicalbenchmark that may correspond to a return of the stocks within ageographic region associated with the stock. Alternatively, thebenchmark return may be configured by a user. Other benchmark returnsmay be utilized.

According to an embodiment wherein benchmark relative return module 214applies a fixed benchmark hurdle, the return rate may need to beadjusted to a length of a recommendation to yield an acceptablebenchmark hurdle. For example, if the fixed benchmark hurdle is 5%annually, but the recommendation is only for a week, a 1% return on thestock for the week of the recommendation would appear to fall short ofthe 5% benchmark hurdle by 4%. However, since the benchmark hurdle wasspecified in annual teens, the benchmark hurdle may be decreased to somelower number for shorter periods of time. For instance, the benchmarkhurdle of 5% annually may be lowered to 0.5% for one week, or otherwiseadjusted. This adjustment of the benchmark hurdle for time may benon-linear. In other embodiments, for recommendations that correspond toa longer period of time than the benchmark hurdle, the benchmark hurdlemay be increased to adjust for the time difference. Although fixedbenchmark hurdles are generally described in terms of an annual return,it will be appreciated that the fixed benchmark hurdle may correspond toany amount of time, and may be adjusted from that time to correspond toa time period associated with a recommendation.

In some embodiments of the invention, benchmark-relative return module214 may adjust the fixed benchmark hurdle according to a benchmarkadjustment equation. Adjusting the fixed benchmark hurdle according tothe benchmark adjustment equation may enhance benchmark-relative returndetermination by benchmark-relative return module 214. The following isan exemplary embodiment of a fixed benchmark return equation:

{circumflex over (B)}=(1+B)^((t/T))−1,   (1)

where B hat is the benchmark hurdle adjusted for the length of therecommendation, B is the percent return associated with the benchmarkhurdle, which is 5% per year in this example, t is the length of therecommendation, in weekdays, and T is the number of weekdays in thelength of the time period associated with the benchmark hurdle, which inthis case is 261 (one year).

In some embodiments of the invention, a plurality of scores stored atscore storage module 224 may be aggregated by score determination module218 into a single score. Aggregating the plurality of scores may providea collective indicator of the performance of the plurality ofrecommendations. In some instances, a plurality of scores associatedwith recommendations made by a contributor with respect to a stock overa selected time period may be aggregated. For example, a contributor maycover a stock for a twelve month period with a positive recommendationthat may last for the first six months, and a negative recommendationthat may last the rest of the twelve month period. Aggregating thescores for both recommendations made by the contributor may provide amore complete indicator of a past performance of the contributor inpredicting then-future valuation of the stock.

In other embodiments, the plurality of recommendations may include aplurality of recommendations made by a plurality of contributors withrespect to a single stock. For instance, a plurality of analystsassociated with a broker may cover the single stock for a period oftime. A broker-level score, in such cases, may include aggregating thescores associated with the single stock for each analyst associated withthe broker over the time period. The aggregate scores for each analystmay then be aggregated to provide a score related to a past performanceof the broker as a whole. Or, the broker may cover the single stock overthe period of time with different analysts. For example, a first analystassociated with the broker may make one or more recommendations on thesingle stock for a beginning portion of the time period, but may make nofurther recommendations for the time period. A second analyst associatedwith the broker may begin to make one or more recommendations for thesingle stock when the first analyst stops making recommendations on thesingle stock at the end of the beginning portion of the time period, andmay cover the single stock for the remainder of the time period. In suchinstances, a broker-level score for the time period may include anaggregation of the score of the first analyst for the beginning portionof the time period with the score of the second analyst over theremainder of the time period.

Another aspect of the invention relates to a roll-up procedure that maybe performed by score determination module 218. The roll-up proceduremay enable a stock set level score related to a set of stocks includedin a stock set to be created from a set of scores measured and createdat the single stock level. The stock set may include a set of stocksselected by a user, included in a market index, a set of stocksdetermined automatically, or other sets of stocks. Since a singlecontributor may not cover, or make recommendations on, each stock withinthe set of stocks, a set of scores that correspond to recommendationsmade by a set of contributors, such as a set of analysts within a singlebroker, a set of brokers, or other sets of contributors, with respect tothe set of stocks may be rolled-up to create a stock set level score.More specifically, each contributor in the set of contributors may coverone or more of the stocks in the stock set and a score measured andcreated at the single stock level may be created for each of therecommendations made on the stocks within the set of stocks made by thecontributors. The resulting set of scores created at the single stocklevel may be rolled-up to represent a stock set level score of therecommendations made by the set of contributors. Stock set level scoresmay enable the performance of the recommendations made by the set ofcontributors on the stock set to be compared with recommendations madeby another set of contributors on the same stock set. For example, eachset of contributors may include a set of analysts associated with asingle broker, and the stock set level scores may enable the performanceof recommendations made by the brokers on the stock set to be comparedor ranked.

According to another aspect of the invention, system 210 may also enablethe score for a single stock to be normalized by score determinationmodule 218. For example, the normalization may be based on the“opportunity” to earn points. This may allow for a more meaningfulrelative comparison of performance on one stock to that of the samecontributor, or a different contributor, on a different stock. In someinstances, this may enable a comparison between contributors or groupsof contributors that cover different or partially overlapping sets ofstocks. For example, a first contributor may cover a first stock set anda second contributor may cover a second stock set. The first stock setmay include a first stock that is not included in the second stock set.If the first stock experienced an extreme change in price during a timeperiod, then the first contributor may have had an increased opportunityfor a high score during the time period with respect to an opportunitythe second contributor that did not cover the first stock. Normalizingthe scores of the first contributor's coverage of the first stock setand the scores of the second contributor's coverage of the second stockset for the time period, before comparing the scores, may provide a moreaccurate comparison of the past performance of the first contributor andthe second contributor. Normalizing scores of recommendations may alsohave the benefit of allowing a rollup of scores by score determinationmodule 218. The normalization of a score may be based on one or morecharacteristics of a stock associated with the score.

In an exemplary embodiment, normalizing a score according to a degree ofvolatility may be performed according to the following equation:

P _(sx) ={circumflex over (P)} _(sx) /RD _(st),   (1)

where {circumflex over (P)}_(sx) may represent an pre-normalizationscore and RD_(st) may represent a risk divisor. The risk divisor may bedetermined as the average absolute excess return of the stock over thebenchmark. It may be capped by a minimum and maximum since very smallnumbers may raise a small divisor issue and very large numbers mayexaggerate a riskiness of stocks that have been volatile recently. Therisk divisor may be determined according to the following equation:

$\begin{matrix}{{{RD}_{st} = {{MAX}\left( {{\hat{D}}_{\min},{{MIN}\left( {{\hat{D}}_{\max},\frac{\sum\limits_{t = 1}^{n}\; {{R_{st} - B_{st}}}}{n}} \right)}} \right)}},} & (1)\end{matrix}$

where

{circumflex over (D)}_(min)=D_(min)*√{square root over ( 1/12 and{circumflex over (D)}_(max)=D_(max)*√{square root over ( 1/12, and wheret goes from 1 to n months. In this case, n may represent a number ofmonths for which a contributor had any coverage on the stock, excludingthe current month if it has not yet ended. In other words, the stock'sreturn for an entire month may be used if the contributor had anycoverage during that month, unless the month has not yet ended. Theminimum and maximum risk divisors may be adjusted so they are on amonthly, rather than annual, basis.

FIG. 3 illustrates an exemplary embodiment of a graphical user interface308 for accessing system 210 via interface module 226. Graphical userinterface 308 may include a graphical user interface. Graphical userinterface 308 may include a score display field 310. Score display field310 may enable a user to view one or more scores. Graphical userinterface 308 may include a contributor field 312 that may enable theuser to view and/or select one or more contributors. A selectcontributor button 314 may enable the user to select one or moredifferent contributors for evaluation, or may activate a contributorselection made by the user at contributor field 312. Graphical userinterface 308 may include a stock field 316 that may enable the user toview and/or select one or more stocks. A select stock button 318 mayenable the user to select one or more different stocks for evaluation,or may activate a stock selection made by the user at stock field 316. Atime period field 320 may enable the user to view and/or select a timeperiod. Interface 226 may include an adjust time period button 322 thatmay enable the user to select a different time period for evaluation, ormay activate a time period selection made at time period field 320.

In some embodiments, graphical user interface 308 may include a payofffunctions field 324. Payoff functions field 324 may provide the userwith information regarding one or more payoff functions being used tocreate a score. Information regarding the payoff functions may include agraphical display of the payoff functions, a display of numeric valuesrelated to one or more parameters of the payoff functions, or otherinformation. Graphical user interface 308 may include an adjustparameters button 326. Adjust parameters button 326 may enable the userto adjust one or more of the parameters of one or more of the payofffunctions, such as, a zero cross, a hurdle, an intersection, a slope, amaximum value, a minimum value, a function shape, or other parameters.Adjusting the parameters may include creating and storing a preferredparameter profile. One or more preferred parameter profiles may bestored within storage section 224 by the user. The preferred parameterprofiles may be recalled by the user via graphical user interface 308 tobe create scores at score determination module 218. For example, theuser may cause more than one preferred parameter profile to be appliedto payoff functions for creating multiple scores with respect to asingle recommendation or recommendations. An adjust benchmark button 328provided by graphical user interface 308 may enable a user to adjust abenchmark used to determine a benchmark-relative return for creating ascore. In a preferred embodiment, a benchmark selection may be stored ina preferred parameter profile. This may enable a previously storedbenchmark to be selected by invoking a preferred parameter profile.

It will be appreciated that the exemplary embodiment of graphical userinterface 308 illustrated in FIG. 3 may not include an exhaustivecollection of functionalities that may be made accessible to the uservia graphical user interface in other contemplated embodiments. Forexample, other functionality may be provided to the user via an otherfunctionality button 330 and/or an other functionality field 332.Alternatively, graphical user interface 308 may also be configured toprovide less functionality to the user.

FIG. 5 illustrates an exemplary embodiment of a method of creating ascore for a single recommendation. The method may include arecommendation information operation 510, a stock return determinationoperation 512, a benchmark return determination operation 514, abenchmark-relative return determination operation 516, and a scoredetermination operation 518.

At recommendation information operation 510, recommendation informationmay be acquired. Recommendation information may include a recommendation(e.g. positive, negative, neutral, etc.), a stock associated with therecommendation, a time period associated with the recommendation, orother recommendation information.

At stock return determination operation 512, a return of the stockassociated with the recommendation may be determined. The return of thestock may be determined for the time period associated with therecommendation.

A benchmark return may be determined at benchmark return determinationoperation 514. The benchmark return may be determined for the timeperiod associated with the recommendation.

At benchmark-relative return operation 516 the benchmark-relative returnmay be determined. The benchmark-relative return may be determined bysubtracting the return of the benchmark from the return of the stock.

A score for the recommendation may be determined at score determinationoperation 518. The score may be determined by applying a payoff functionthat corresponds with the recommendation to the benchmark-relativereturn.

FIG. 6 illustrates an exemplary embodiment of a method of determining anaggregated score associated with a plurality of recommendations on asingle stock. The method may include a recommendation acquisitionoperation 608, an individual score determination operation 610, anindividual score summing operation 612, an individual score completionoperation 614, an individual score averaging operation 616, and anormalizing operation 618.

The plurality of recommendations may be acquired at recommendationacquisition operation 608. The recommendations may be acquired byselection, or they may be automatically acquired.

At individual score determination operation 610, a score for anindividual recommendation from the plurality of recommendations may bedetermined. The score for the individual recommendation may bedetermined according to the method of creating a score for a singlerecommendation.

The score for the individual recommendation may be combined withpreviously determined scores at individual score summing operation 612.The individual recommendations may be combined by addition. In someembodiments, the scores may be weighted before being included in thesum. The scores may be weighted according to a length of a time periodfor which the recommendations the scores are associated with were made.For example, a score for a recommendation for one day may be weighteddifferently than a score for a recommendation for 364 days.

At individual score completion determination operation 614 adetermination may be made as to whether all of the recommendations inthe plurality of recommendations have been scored and summed. If all ofthe recommendations have not been scored and summed, the method mayreturn to individual score determination operation 610 to determine ascore for another individual recommendation. If all of therecommendations have been scored and summed, the method may proceed to616 individual score averaging operation.

The sum of the scores of the individual recommendations may be averagedat individual score averaging operation 616. The scores may be averagedby dividing the sum of the scores by the number of recommendations inthe plurality of recommendations. In some embodiments where the scoresare not weighted before being summed at operation 612, the sum of thescores may be divided by a weighted number of recommendations. Forexample, each recommendation may not be counted as one depending on alength of a time period associated with the recommendation.

At normalizing operation 618, the averaged score may be normalized. Theaveraged score may be normalized by the opportunity associated with thestock for the time periods associated with the recommendations.

FIG. 4 illustrates an exemplary embodiment of a method of determiningscore for recommendations made by a single contributor on a single stock(stock A). The method may include a stock value determination operation410. Stock value determination operation 410 may determine the value forstock A from market information at predetermined intervals, such as adaily interval, or other intervals. Value information for stock A may beassembled in a format similar to an exemplary embodiment illustrated byTable 1.

TABLE 1 Date Day 1 Day 2 Day 3 Day 4 Day 5 Price 5 3 4 5 2.5 Adjust forsplit 2.5 1.5 2 2.5 2.5

The method may include a recommendation determination operation 412. Atrecommendation determination operation 412 recommendation informationrelated to the recommendations by the contributor for stock A may beassembled. The recommendation information assembled by recommendationdetermination operation 412 may assembled in a format similar to anexemplary embodiment illustrated by Table 2.

TABLE 2 Stock Rec. Start Date End Date A Neutral Dec. 20, 2003 Feb. 4,2003 A Positive Feb. 4, 2003 Jun. 25, 2003 A Stop Jun. 25, 2003 Jul. 28,2003 A Positive Jul. 28, 2003 Sep. 25, 2003 A Stop Sep. 25, 2003 Sep.28, 2003 A Positive Sep. 28, 2003 Nov. 2, 2003 A Negative Nov. 2, 2003still active

The method may include a time period-return correlation operation 414.At time period-return correlation operation 414 time period informationassembled at recommendation determination operation 412 may beintervalized with stock value information gather at stock pricedetermination operation 410. The intervalized time period-return dataproduced by time period-return operation 414 may be intervalizedaccording to an exemplary embodiment illustrated by Table 3. At timeperiod-return correlation operation 414 a stock return for stock A maybe determined for each recommendation period by determining a differencebetween a stock value for stock A at the beginning of the recommendationperiod and a stock value for stock A at the end of the recommendationperiod.

TABLE 3 Interval Relative Stock Interval # Start Date stock pricesReturn Interval 1 Dec. 31, 2002 1 0% Interval 2 Jan. 3, 2003 1.15 15%Interval 3 Jan. 31, 2003 1.3 13% Interval 4 Feb. 4, 2003 1.2 −8%Interval 5 Feb. 28, 2003 1.2 0% Interval 6 Mar. 31, 2003 1.3 8% Interval7 Apr. 30, 2003 1.4 8% Interval 8 May 31, 2003 1.3 −7% Interval 9 Jun.25, 2003 1.1 −15% Interval 10 Jun. 30, 2003 1.2 9% Interval 11 Jul. 28,2003 1.4 17% Interval 12 Jul. 31, 2003 1.5 7% Interval 13 Aug. 30, 20031.6 7% Interval 14 Sep. 25, 2003 1.7 6% Interval 15 Sep. 28, 2003 1.6−6% Interval 16 Sep. 30, 2003 1.5 −6% Interval 17 Oct. 31, 2003 1.5 0%Interval 18 Nov. 2, 2003 1.7 13% Interval 19 Nov. 30, 2003 1.4 −18%Interval 20 Dec. 31, 2003 1.5 7%

The method may include a benchmark return determination operation 416.At benchmark return determination operation 416 a benchmark return foreach recommendation interval may be determined. The benchmark return foreach recommendation may be associated with the other informationpreviously acquired for the each recommendation in a manner similar toan exemplary embodiment illustrated in Table 4.

TABLE 4 Weekdays in call Stock Benchmark Rec. # Rec. interval ReturnReturn Start Date End Date 1 Neutral 35 30.0% 2% Dec. 31, 2002 Feb. 4,2003 2 Positive 141  0.0% 10% Feb. 4, 2003 Jun. 25, 2003 3 Stop 33 −7.7%20% Jun. 25, 2003 Jul. 28, 2003 4 Positive 59 33.3% 5% Jul. 28, 2003Sep. 25, 2003 5 Stop 3   6% −5% Sep. 25, 2003 Sep. 28, 2003 6 Positive35 −11.8%   2% Sep. 28, 2003 Nov. 2, 2003 7 Negative 59 −6.7% −5% Nov.2, 2003 Dec. 31, 2003

The method may include an interval score determination operation 418. Atinterval score determination operation 418 a score related to eachrecommendation made on stock A by the contributor may be created via apayoff function. The score for each recommendation may be associatedwith other information related to the recommendation similar to anexemplary embodiment illustrated in Table 5.

TABLE 5 Weekdays in call Stock Benchmark Call # Call interval ReturnReturn Start Date End Date Payout 1 Neutral 22 30.0% 2% Dec. 31, 2002Feb. 4, 2003 −0.0043 2 Positive 101  0.0% 10% Feb. 4, 2003 Jun. 25, 2003−0.2200 3 Positive 71 17.65%  7% Jul. 28, 2003 Nov. 2, 2003 0.0995 4Negative 43 −6.7% −5% Nov. 2, 2003 Dec. 31, 2003 0.0167

The method may include a score aggregation operation 420. At scoreaggregation operation 420 the scores for each recommendation may beaggregated to determine an aggregated score. Aggregating the scores mayinclude averaging the scores, or otherwise aggregating the scores. Inthe embodiment illustrated in Table 5, the aggregate score would be−0.06105.

The method may include a score normalization operation 422. At scorenormalization operation 422, the aggregate score may be normalized. Theaggregate score may be normalized according to the opportunity factorassociated with the time periods associated with the aggregate score.

1-25. (canceled)
 26. A computer-implemented method of scoring at leastone contributor for the performance of the contributor's recommendationwith respect to a single stock, the method being implemented in acomputer system, the method comprising: executing one or more computerprogram modules on one or more processors of the computer system tocreate a score for a contributor's recommendation on a stock relative toa benchmark, the score being determined based on a payoff function thatdetermines the score as a function of a plurality of factors, thefactors comprising: i) a benchmark-relative return on the stock, whereinthe benchmark-relative return of the stock is a return on the stockrelative to a return on a benchmark that corresponds to the stock; andii) the recommendation made by the contributor; wherein the payofffunction for a neutral recommendation on the stock has a maximum scoreat a baseline benchmark-relative return, and has scores that decreaseaway from the maximum score for benchmark-relative returns greater thanand less than the baseline benchmark relative return; and storing thescore in one or more electronic storage media associated with theserver.
 27. The method of claim 26 wherein the recommendation isselectable by the contributor from a group of at least three types ofrecommendations, including a positive recommendation, a neutralrecommendation and a negative recommendation, and wherein the payofffunction for the positive recommendation is a function having a positiveslope with respect to the benchmark-relative return and the payofffunction for the negative recommendation is a function having a negativeslope with respect to the benchmark-relative return.
 28. The method ofclaim 26 wherein the recommendation is selectable by the contributorfrom a group of at least five types of recommendations, including apositive recommendation, a more positive recommendation, a neutralrecommendation, a negative recommendation and a more negativerecommendation, and wherein the payoff function for the positiverecommendation is a function having a first positive slope with respectto benchmark-relative return and a first positive function baselinevalue at the baseline benchmark-relative return, the payoff function forthe more positive recommendation is a function having a second positiveslope with respect to benchmark-relative return greater than the firstpositive slope and a second positive function baseline value that isoffset in a positive direction from the first positive function baselinevalue along a benchmark-relative return axis, the payoff function forthe negative recommendation is a function having a first negative slopewith respect to benchmark-relative return and a first negative functionbaseline value at the baseline benchmark-relative return, and the payofffunction for the more negative recommendation is a function having asecond negative slope with respect to benchmark-relative return morenegative than the first negative slope and a second negative baselinevalue that is offset in a negative direction from the first negativefunction baseline value along the benchmark-relative return axis. 29.The method of claim 26, wherein the baseline benchmark-relative returnis zero.
 30. The method of claim 26, wherein the benchmark includes avariable benchmark, including one of a sector benchmark, an industrybenchmark, a geographic benchmark or a coverage benchmark.
 31. Themethod of claim 26, wherein the contributor is an analyst, and theanalyst is associated with a broker and the benchmark comprises abroker-specific benchmark.
 32. The method of claim 26, wherein thecontributor is an analyst, and the analyst is associated with a brokerand where different brokers may associate recommendations with differentnumerical scales, the method further comprising the step ofstandardizing a recommendation to a common numerical scale.
 33. Themethod of claim 26, further comprising executing one or more programmodules on the one or more processors of the computer system tonormalize the score for the contributor's recommendation for the stock.34. The method of claim 26, further comprising executing one or moreprogram modules on the one or more processors of the computer system tonormalize the score for the contributor's recommendation for the stockbased on the volatility of the stock.
 35. The method of claim 26,further comprising executing one or more program modules on the one ormore processors of the computer system to aggregate the score for thecontributor's recommendation for the stock with one or more other scoresfor recommendations made by the contributor on the stock to create astock-level score, executing one or more program modules on the one ormore processors of the computer system to normalize the stock-levelscore to create a normalized stock-level score, and executing one ormore program modules on the one or more processors of the computersystem to create a stock set-level score by combining the normalizedstock-level score with at least one other normalized stock-level scorefor recommendations made on another stock.
 36. The method of claim 26,wherein a score is based at least in part on a period of time andfurther comprising executing one or more program modules on the one ormore processors of the computer system to adjust an contributor's scoreif the contributor's recommendation covers only a portion of the periodof time.
 37. A system configured to score at least one contributor forthe performance of the contributor's recommendation with respect to asingle stock, the score for the recommendation depending on a pluralityof factors, including the performance of the stock, the performance of abenchmark and the recommendation made by the contributor, the systemcomprising: a server configured to access, over a network, one or moreelectronic storage media storing information relating to stockperformance, benchmark performance and contributor's recommendations;and one or more processors associated with the server configured tocreate a score for a recommendation of a contributor on an individualstock based on a payoff function, wherein the payoff function for aneutral recommendation on the stock has a maximum in score at a baselinebenchmark-relative return on the stock, and has scores that decreaseaway from the maximum score for benchmark-relative returns greater thanand less than the baseline benchmark relative return, wherein thebenchmark-relative return on the stock is a return on the stock relativeto a return on a benchmark that corresponds to the stock.
 38. The systemof claim 37 wherein the recommendation is selectable by the contributorfrom a group of at least three types of recommendations, including apositive recommendation, a neutral recommendation and a negativerecommendation, and wherein the payoff function for the positiverecommendation is a function having a positive slope with respect tobenchmark-relative return and the payoff function for the negativerecommendation is a function having a negative slope with respect tobenchmark-relative return.
 39. The system of claim 37 wherein therecommendation is selectable by the contributor from a group of at leastfive types of recommendations, including a positive recommendation, amore positive recommendation, a neutral recommendation, a negativerecommendation and a more negative recommendation, and wherein thepayoff function for the positive recommendation is a function having afirst positive slope with respect to benchmark relative return and afirst positive function baseline value at the baselinebenchmark-relative return, the payoff function for the more positiverecommendation is a function having a second positive slope with respectto benchmark relative return greater than the first positive slope and asecond positive function baseline value that is offset in a positivedirection from the first positive function baseline value along abenchmark-relative return axis, the payoff function for the negativerecommendation is a function having a first negative slope with respectto benchmark relative return and a first negative function baselinevalue at the baseline percent benchmark-relative return, and the payofffunction for the more negative recommendation is a function having asecond negative slope with respect to benchmark relative return morenegative than the first negative slope and a second negative baselinevalue that is offset in a negative direction from the first negativefunction baseline value along the benchmark-relative return axis. 40.The system of claim 37, wherein the benchmark includes a fixed benchmarkor a variable benchmark.
 41. The system of claim 37, wherein thebaseline benchmark-relative return is zero.
 42. The system of claim 37,wherein the contributor is an analyst, and the analyst is associatedwith a broker and the benchmark comprises a broker-specific benchmark.43. The system of claim 37, wherein the contributor is an analyst, andthe analyst is associated with a broker and where different brokers mayassociate recommendations with different numerical scales, the one ormore processors further being configured to standardize a recommendationto a common numerical scale.
 44. The system of claim 37, wherein the oneor more processors are further configured to normalize the score for thecontributor's recommendation for the stock.
 45. The system of claim 37,wherein the processing section within the server is configured toexecute the computer software program to normalize the score for thecontributor's recommendation for the stock based on the volatility ofthe stock.
 46. The system of claim 37, wherein the one or moreprocessors are configured to aggregate the score for the contributor'srecommendation for the stock with one or more other scores forrecommendations made by the contributor on the stock to create astock-level score, to normalize the stock-level score to create anormalized stock-level score, and to create a stock set-level score bycombining the normalized stock-level score with at least one othernormalized stock-level score for recommendations made on another stock.47. The system of claim 37, wherein a score is based at least in part ona period of time and further wherein the one or more processors arefurther configured to adjust an contributor's score if the contributor'srecommendation covers only a portion of the period of time.
 48. Thesystem of claim 37, wherein the one or more processers are furtherconfigured to generate a display conveying the score.